Barriers, Facilitators, and Intention to Use Artificial Intelligence (AI) for Breast Cancer Diagnosis: A Mixed-Methods Study Among Austrian Physicians With and Without AI Experience (Preprint)

BACKGROUND Artificial intelligence (AI) has demonstrated strong potential in breast cancer diagnostics by improving accuracy, efficiency, and clinical workflow. However, adoption among physicians remains variable. Existing research often overlooks the contextual and experiential differences between clinicians who use AI and those who do not. A comprehensive understanding of barriers and facilitators, especially across user groups, is essential to inform equitable and effective AI implementation in real-world settings. OBJECTIVE This study aimed to (1) identify key barriers and facilitators influencing the use of AI tools in breast cancer diagnostics, with a specific focus on comparing current users and non-users, and (2) examine how social, technological, and individual-level factors predict physicians’ attitudes toward AI, intention to use it, and perceived likelihood of future adoption METHODS A cross-sectional, embedded mixed-methods survey was conducted with 46 Austrian physicians. Quantitative items were based on the Technology Acceptance Model (TAM) and its extensions. Open-ended responses were analysed using conventional content analysis and integrated with quantitative results via joint displays. Ordinary least squares regressions identified predictors of attitudes, intention, and likelihood of future AI use. RESULTS Among the 46 participating physicians, 50% reported current AI use. Common facilitators included improved quality of work, efficiency and expanding knowledge. Non-users highlighted barriers such as limited access (81%), high costs, and lack of training. Despite these differences, both groups expressed strong future adoption intentions. Perceiving multiple facilitators was significantly associated with more favourable attitudes (B = 0.83, p = .017), stronger intention to use AI (B = 1.32, p = .014), and higher perceived likelihood of future use (B = 1.56, p = .001). AI-related skills positively predicted intention (B = 1.00, p = .040) and likelihood of future use (B = 1.16, p = .011), while colleagues’ positive views about AI predicted both attitudes (B = 0.34, p = .021) and intention (B = 0.39, p = .010). In contrast, perceiving multiple barriers was associated with lower intention (B = –0.84, p = .047) and likelihood (B = –1.48, p < .001). Being aged 50 or older was significantly associated with more negative attitudes (B = –1.11, p = .002) and lower likelihood of future use (B = –0.82, p = .021). CONCLUSIONS This study offers preliminary insights into the implementation of AI in breast cancer diagnostics within the Austrian healthcare context. AI adoption appears to be a staged process with evolving support needs. Early-stage users may benefit from improved access and training, while experienced users require support for workflow integration and trust-building. Promoting peer support, addressing demographic disparities, and embedding AI training into clinical routines may support more sustainable and equitable adoption. These findings inform tailored implementation strategies and offer recommendations that may be transferable to other health systems.